The Cosmos LLaMa Instruct is designed for text generation tasks, providing the ability to continue a given text snippet in a coherent and contextually relevant manner. Due to the diverse nature of the training data, which includes websites, books, and other text sources, this model can exhibit biases. Users should be aware of these biases and use the model responsibly.

Transformers pipeline

python

import transformers
import torch

model_id = "ytu-ce-cosmos/Turkish-Llama-8b-Instruct-v0.1"

pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device_map="auto",
)

messages = [
    {"role": "system", "content": "Sen bir yapay zeka asistanısın. Kullanıcı sana bir görev verecek. Amacın görevi olabildiğince sadık bir şekilde tamamlamak. Görevi yerine getirirken adım adım düşün ve adımlarını gerekçelendir."},
    {"role": "user", "content": "Soru: Bir arabanın deposu 60 litre benzin alabiliyor. Araba her 100 kilometrede 8 litre benzin tüketiyor. Depo tamamen doluyken araba kaç kilometre yol alabilir?"},
]

terminators = [
    pipeline.tokenizer.eos_token_id,
    pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

outputs = pipeline(
    messages,
    max_new_tokens=256,
    eos_token_id=terminators,
    do_sample=True,
    temperature=0.6,
    top_p=0.9,
)
print(outputs[0]["generated_text"][-1])

Transformers AutoModelForCausalLM

python

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "ytu-ce-cosmos/Turkish-Llama-8b-Instruct-v0.1"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {"role": "system", "content": "Sen bir yapay zeka asistanısın. Kullanıcı sana bir görev verecek. Amacın görevi olabildiğince sadık bir şekilde tamamlamak. Görevi yerine getirirken adım adım düşün ve adımlarını gerekçelendir."},
    {"role": "user", "content": "Soru: Bir arabanın deposu 60 litre benzin alabiliyor. Araba her 100 kilometrede 8 litre benzin tüketiyor. Depo tamamen doluyken araba kaç kilometre yol alabilir?"},
]

input_ids = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

outputs = model.generate(
    input_ids,
    max_new_tokens=256,
    eos_token_id=terminators,
    do_sample=True,
    temperature=0.6,
    top_p=0.9,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))

Acknowledgments

Thanks to the generous support from the Hugging Face team, it is possible to download models from their S3 storage 🤗
Computing resources used in this work were provided by the National Center for High Performance Computing of Turkey (UHeM) under grant numbers 1016912023 and 1018512024
Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC)

Contact

COSMOS AI Research Group, Yildiz Technical University Computer Engineering Department
https://cosmos.yildiz.edu.tr/
cosmos@yildiz.edu.tr

Citation

bibtex

@inproceedings{kesgin2024optimizing,
  title={Optimizing Large Language Models for Turkish: New Methodologies in Corpus Selection and Training},
  author={Kesgin, H Toprak and Yuce, M Kaan and Dogan, Eren and Uzun, M Egemen and Uz, Atahan and {\.I}nce, Elif and Erdem, Yusuf and Shbib, Osama and Zeer, Ahmed and Amasyali, M Fatih},
  booktitle={2024 Innovations in Intelligent Systems and Applications Conference (ASYU)},
  pages={1--6},
  year={2024},
  organization={IEEE}
}

ytu-ce-cosmos/Turkish-Llama-8b-Instruct-v0.1

Cosmos LLaMa Instruct

Transformers pipeline

Transformers AutoModelForCausalLM

Acknowledgments

Contact

Citation

license: llama3

ytu-ce-cosmos/
Turkish-Llama-8b-Instruct-v0.1